Suchergebnisse

AbstractDoes collective citizen input impact government priorities and performance in service provision? As cities increasingly offer interactive issue reporting options through online platforms and mobile apps, I investigate whether comments and follows on requests drive faster issue resolution. I theorize that this input signals issue validity, severity, or scrutiny, assisting city administrators in prioritizing and responding to requests. Leveraging a novel dataset of requests from 100 cities, I find that comments and follows double the probability of request closure and that collaborative requests are resolved up to 5 days faster on average than non‐collaborative requests. By comparing two cities that use the same platform but that differ in the observability of citizen collaboration, I isolate a distinct and significant influence of collective citizen input on government responsiveness. The findings speak to how technological advances enable information‐sharing from citizens that can shape service delivery rules and outcomes.

AbstractAre narratives as influential in gaining the attention of policymakers as expert information, including for complex, technical policy domains such as artificial intelligence (AI) policy? This pre‐registered study uses a field experiment to evaluate legislator responsiveness to policy entrepreneur outreach. In partnership with a leading AI think tank, we send more than 7300 U.S. state legislative offices emails about AI policy containing an influence strategy (providing a narrative, expert information, or the organization's background), along with a prominent issue frame about AI (emphasizing technological competition or ethical implications). To assess engagement, we measure link clicks to further resources and webinar registration and attendance. Although AI policy is a highly technical domain, we find that narratives are just as effective as expert information in engaging legislators. Compared to control, expert information and narratives led to 28 and 34 percent increases in policymaker engagement, respectively. Furthermore, higher legislature professionalism and lower state‐level prior AI experience are associated with greater engagement with both narratives and expert information. Finally, we find that policymakers are equally engaged by an ethical framing of AI policy as they are with an economic one. The findings advance efforts to bridge scholarship on policy narratives, policy entrepreneurship, and agenda‐setting.

AbstractEvidence suggests that well-funded, professional legislatures more effectively provide constituents with their preferred policies and may improve social welfare. Yet, legislative resources across state legislatures have stagnated or dwindled at least in part due to public antagonism toward increasing representatives' salaries. We argue that one reason voters oppose legislative resources, like salary and staff, is that they are unaware of the potential benefits. Employing a pre-registered survey experiment with a pre–post design, we find that subjects respond positively to potential social welfare benefits of professionalization, increasing support for greater resources. We also find that individuals identifying with the legislative majority party respond positively to potential responsiveness benefits and that out-partisans do not respond negatively to potential responsiveness costs. In a separate survey of political elites, we find similar patterns. These results suggest that a key barrier to increasing legislative professionalism – anticipated public backlash – may not be insurmountable. The findings also highlight a challenge of institutional choice: beliefs that representatives are unresponsive or ineffective lead to governing institutions that may ensure these outcomes.

Can features of surveys, such as question ordering and informational stimuli, affect respondent self-reported partisanship? We report the results of two studies to examine how the survey environment affects the probability that a respondent identifies with a particular party and presidential approval conditional on self-reported partisanship. In an original experiment, we find that, under some circumstances, a question-ordering treatment increases Republican partisanship. The estimated effects are statistically different from zero in unweighted specifications where leaners are excluded from the definition of Republicans and also among respondents who identified as Republicans in previous survey waves. In our second study, we show that an informational intervention reduces the probability that a respondent identifies as an Independent and that including post-treatment partisanship in a regression changes the estimated treatment effect of informational interventions on presidential approval. Our analysis shows that seemingly minor features of survey design can affect self-reported partisanship, average outcomes for different partisans, and treatment effect estimates. We discuss the implications of our findings for survey design and theories of partisan identification and presidential approval.

AbstractIn the context of rising delegation of administrative discretion to advanced technologies, this study aims to quantitatively assess key public values that may be at risk when governments employ automated decision systems (ADS). Drawing on the public value failure framework coupled with experimental methodology, we address the need to measure and compare the salience of three such values—fairness, transparency, and human responsiveness. Based on a preregistered design, we administer a survey experiment to 1460 American adults inspired by prominent ADS applications in child welfare and criminal justice. The results provide clear causal evidence that certain public value failures associated with artificial intelligence have significant negative impacts on citizens' evaluations of government. We find substantial negative citizen reactions when fairness and transparency are not realized in the implementation of ADS. These results transcend both policy context and political ideology and persist even when respondents are not themselves personally impacted.

This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf" over fake news. Strategic and false claims that stories are fake news or deepfakes may benefit politicians by helping them maintain support after a scandal. We posit that this benefit, known as the "liar's dividend," may be achieved through two politician strategies: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. We administer five survey experiments to over 15,000 American adults detailing hypothetical politician responses to stories describing real politician scandals. We find that claims of misinformation representing both strategies raise politician support across partisan subgroups. These strategies are effective against text-based reports of scandals, but are largely ineffective against video evidence and do not reduce general trust in media. Finally, these false claims produce greater dividends for politicians than alternative responses to scandal, such as remaining silent or apologizing.

AbstractLaw enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.

Abstract
Does providing information about police shootings influence policing reform preferences? We conducted an online survey experiment in 2021 among approximately 2,600 residents of 10 large US cities. It incorporated original data we collected on police shootings of civilians. After respondents estimated the number of police shootings in their cities in 2020, we randomized subjects into three treatment groups and a control group. Treatments included some form of factual information about the police shootings in respondents' cities (e.g., the actual total number). Afterward, respondents were asked their opinions about five policing reform proposals. Police shooting statistics did not move policing reform preferences. Support for policing reforms is primarily associated with partisanship and ideology, coupled with race. Our findings illuminate key sources of policing reform preferences among the public and reveal potential limits of information-driven, numeric-based initiatives to influence policing in the US.